Comparison of Higuchi, Katz and Multiresolution Box-Counting Fractal Dimension Algorithms for EEG Waveform Signals Based on Event-Related Potentials

Comparison of Higuchi, Katz and Multiresolution Box-Counting Fractal Dimension Algorithms for EEG Waveform Signals Based on Event-Related Potentials

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Santiago Fernández Fraga
Jaime Rangel Mondragón


Obtaining information through the measurement of brain signals recorded during different processes or physiological conditions is important for developing computer interfaces that translate electrical brain signals to computer control commands. Electroencephalography (EEG) records the electrical activity of the brain in response to its receipt of different external stimuli (potential events). Analysis of these signals makes it possible to identify and distinguish specific states of physiological brain function. The Fractal Dimension has been used as a tool for biomedical waveform analysis, in particular to measure the complexity of time series generated by EEG. This paper aims to analyze a database (HeadIT) of biomedical time series obtained by EEG for which the fractal dimension will be obtained by the Higuchi, Katz and multiresolution box-counting methods, showing the relationship between the method for obtaining the fractal dimension and the physiological condition of the brain event-related potentials.

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